Interacting Complexities of Herds and Social Organizations: Agent Based Modeling

156 Pages·2020·6.642 MB·English

by  Stanislaw Raczynski

Evolutionary Economics and Social Complexity Science 19
Stanislaw Raczynski
Interacting
Complexities of
Herds and Social
Organizations
Agent Based Modeling
Evolutionary Economics and Social Complexity
Science
Volume 19
Editors-in-Chief
Takahiro Fujimoto, Tokyo, Japan
Yuji Aruka, Tokyo, Japan
Editorial Board
Satoshi Sechiyama, Kyoto, Japan
Yoshinori Shiozawa, Osaka, Japan
Kiichiro Yagi, Neyagawa, Osaka, Japan
Kazuo Yoshida, Kyoto, Japan
Hideaki Aoyama, Kyoto, Japan
Hiroshi Deguchi, Yokohama, Japan
Makoto Nishibe, Sapporo, Japan
Takashi Hashimoto, Nomi, Japan
Masaaki Yoshida, Kawasaki, Japan
Tamotsu Onozaki, Tokyo, Japan
Shu-Heng Chen, Taipei, Taiwan
Dirk Helbing, Zurich, Switzerland
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More information about this series at http://www.springer.com/series/11930
Stanislaw Raczynski
Interacting Complexities
of Herds and Social
Organizations
Agent Based Modeling
Stanislaw Raczynski
Facultad de Ingeniería
Universidad Panamericana
Ciudad de México, México
ISSN 2198-4204 ISSN 2198-4212 (electronic)
Evolutionary Economics and Social Complexity Science
ISBN 978-981-13-9336-5 ISBN 978-981-13-9337-2 (eBook)
https://doi.org/10.1007/978-981-13-9337-2
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Preface
According to John von Neumann, “by a model is meant a mathematical construct
which, with the addition of certain verbal interpretations, describes observed phe-
nomena. The justification of such a mathematical construct is solely and precisely
that it is expected to work — that is, correctly to describe phenomena from a reason-
ably wide area.” Humans always (sometimes unconsciously) have used models cre-
ated in their brains. When our technical skills have grown, the models acquired the
form of physical, scale models, drawings, and finally sophisticated logical and
mathematical constructions. The common concept of modeling is defined as a sci-
entific activity, the aim of which is to make a particular part or feature of the world
easier to understand.
The complexity of the real world can be modeled to some extent. There are many
definitions of complexity, recently related to “system of systems” structures. Note
that a system that contains a great number of sub-systems or items or a huge number
of differential equations is not necessarily complex. The complexity lies in the way
the components interact with each other and the diversity of system components. In
such systems, the simulation results may provide information about the behavior of
the whole system, which is not the sum of individual behavior patterns. This is also
interpreted as nonlinearity. This book is focused on this kind of modeling and simu-
lation experiments.
Analog and digital computers gave us a powerful tool for model building and
analysis. At the very beginning of the computer era, the differential equations have
been solved on analog machines, helping scientists and engineers to design mecha-
nisms, circuits, and complex devices. The field of model applications has grown
over the decades, including not only the works of engineering and exact sciences but
also the models of animal and human societies.
At the very beginning, model builders have been looking for some kinds of alge-
braic, ordinary, or partial differential equations to describe real system behavior.
The most known and explored field is the System Dynamics (SD) approach that
mainly uses models in the form of ordinary differential equations. However, it
should be noted that this is not the only way to build models. A strange conviction
aroused among the modelers that everything in the real world can be described by
v
vi Preface
differential equations. In general, this is not true. Although the SD methodology is
still widely used and useful, there are other ways for model building, like fuzzy
logic, differential inclusions, discrete event simulation, and agent based models,
among others.
The topic of this book is agent based modeling. The rapid growth of the compu-
tational capacity of new computers permits us to create thousands of objects in
computer memory and make them interact with each other. In agent based models,
the objects are equipped with certain artificial intelligence, can optimize their
behavior, and take decisions. Some systems can be modeled both using differential
equations and agent based approach. The results of these two methods are frequently
quite different, for example, results of the Lotka-Volterra prey-predator model and
the prey-predator agent based model. Here, we will not suggest which of these mod-
els is valid or not. These are just different modeling methods that produce results of
different kind. Undoubtedly, agent based modeling is more flexible and can reflect
more behavioral patterns of the individuals, providing the insight on the macro-
behavior of the system. In Chap. 1, there are comments on some agent based model-
ing tools. The other chapters contain examples of applications to artificial societies
and competing populations of individuals and the growth, interactions, and decay of
organizations and other applications. For reader’s convenience, a short recall about
object- and agent-based modeling is repeated in each chapter. Thus, each chapter
can be read as independent unit. In Chap. 9, you can find a description of an experi-
mental software package that uses the classic continuous system dynamics graphi-
cal user interface (GUI) that is used to construct the model. However, the transparent
simulation engine that runs behind this GUI is discrete event simulation. This way,
we can compare the results of the conventional system dynamics packages with
these provided by discrete event simulation. The relevant differences between these
two simulation paradigms are pointed out.
Mexico City, Mexico Stanislaw Raczynski
Acknowledgements
I would like to express my gratitude to the Editors of the journals listed below for
the permission to use the updated versions of my articles, as follows:
Simulating self-organization and interference between certain hierarchical struc-
tures. Nonlinear Dynamics, Psychology, and Life Sciences, 2014, Vol 18, no 4,
used in Chap. 2 of this book,
A Self-destruction game, Nonlinear Dynamics, Psychology, and Life Sciences,
2006, Vol 10, no 4, used in Chap. 7 of this book,
The spontaneous rise of the herd instinct: agent-based simulation, Nonlinear
Dynamics, Psychology, and Life Sciences, to appear, used in Chap. 5 of this
book.
Simulation of the dynamic interactions between terror and anti-terror organiza-
tional structures, Journal of Artificial Societies and Social Simulation, Vol. 7, no.
2, used in Chap. 3 of this book.
Influence of the gregarious instinct and individuals’ behavior patterns on macro
migrations: simulation experiments, Journal of Human Behavior in the Social
Environment, Vol. 28, no 2, used in Chap. 6 of this book. Visit the journal home
page at www.tandfonline.com.
Stanislaw Raczynski
vii
Contents
1 Agent-Based Models: Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 General Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Discrete Event Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 GPSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Arena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.3 SIMIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.4 Simula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.5 PASION, PSM++, and BLUESSS . . . . . . . . . . . . . . . . . . . . 6
1.3 E xample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.4 C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 S imulating Self-Organization and Interference Between Certain
Hierarchical Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 I ntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 T he Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.1 General Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 Interaction Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Interactions Between Terror and Anti- terror Organizations . . . . . . . . 31
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.1 Interactions Between Structures . . . . . . . . . . . . . . . . . . . . . 36
3.2.2 Simulation Tool and Model Implementation . . . . . . . . . . . . 37
3.2.3 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
ix
x Contents
4 Organization Growth and Decay: Simulating Interactions
of Hierarchical Structures, Corruption and Gregarious Effect . . . . . . 47
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Agent-Based Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 Simulation Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4.1 The Individuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4.2 Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.4.3 Auxiliary Control Process . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.5 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.5.1 Experiment 1: Criterion Function Zero . . . . . . . . . . . . . . . . 57
4.5.2 Experiment 2: Change Criterion – Size . . . . . . . . . . . . . . . . 58
4.5.3 Experiment 3: Corruption Level . . . . . . . . . . . . . . . . . . . . . 59
4.5.4 Experiment 4: Accumulated Corruption . . . . . . . . . . . . . . . 59
4.5.5 Experiment 5: Criterion – Grow Rate (Herd Instinct) . . . . . 61
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5 The Spontaneous Rise of the Herd Instinct: Agent-Based
Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Agent-Based Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.2.1 General Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.2.2 BLUESSS Simulation Package . . . . . . . . . . . . . . . . . . . . . . 70
5.3 T he Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.3.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.3.2 Event: Search for Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.4 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.4.1 Gregarious Factor, Search for Food . . . . . . . . . . . . . . . . . . . 75
5.4.2 The Influence of the Threat . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.5 C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 I nfluence of the Gregarious Instinct and Individuals’ Behavior
Patterns on Macro Migrations: Simulation Experiments . . . . . . . . . . . 83
6.1 I ntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2 Object- and Agent-Based Models . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.3 The Simulation Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.4 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.6 S imilarity to the Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
6.7 C onclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96